Now showing items 1-7 of 7

    • A dataset for troll classification of Tamil memes 

      Chakravarthi, Bharathi Raja; Varma, Pranav; Arcan, Mihael; McCrae, John P.; Buitelaar, Paul; Shardul, Suryawanshi (European Language Resources Association (ELRA), 2020-05-11)
      Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents ...
    • Enhancing multiple-choice question answering with causal knowledge 

      Dalal, Dhairya; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2021-06-10)
      The task of causal question answering aims to reason about causes and effects over a provided real or hypothetical premise. Recent approaches have converged on using transformer-based language models to solve question ...
    • Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text 

      Suryawanshi, Shardul; Chakravarthi, Bharathi Raja; Arcan, Mihael; Buitelaar, Paul (European Language Resources Association (ELRA), 2020-05-11)
      A meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related ...
    • NUIG-DSI at the WebNLG+ challenge: Leveraging transfer learning for RDF-to-text generation 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2020-12-18)
      This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model ...
    • NUIG-DSI’s submission to the GEM Benchmark 2021 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2021-08-05)
      This paper describes the submission by NUIG-DSI to the GEM benchmark 2021. We participate in the modeling shared task where we submit outputs on four datasets for data-to-text generation, namely, DART, WebNLG (en), E2E and ...
    • Towards bootstrapping a chatbot on industrial heritage through term and relation extraction 

      Arcan, Mihael; O’Halloran, Rory; Robin, Cecile; Buitelaar, Paul (Association for Computational Linguistics (ACL), 2022-11-20)
      We describe initial work in developing a methodology for the automatic generation of a conversational agent or ‘chatbot’ through term and relation extraction from a relevant corpus of language data. We develop our ...
    • Utilising knowledge graph embeddings for data-to-text generation 

      Pasricha, Nivranshu; Arcan, Mihael; Buitelaar, Paul (Association for Computational Linguistics, 2020-12-18)
      Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their ...